Method of controlling liquid production utilizing an expert system controller

ABSTRACT

Method for controlling the production of one or more liquid products produced by one or more plants, that can be an air separation plant, that has a liquid storage capacity and that consumes electrical power. Linguistic values related to current liquid inventory, a rate of change of liquid inventory, projected demand requirements and projected unit power costs are inputted into a expert system controller having one or more rule sets that apply such input linguistic values to produce output linguistic values that are converted back to an output numerical value of a forecasted differential production rate to be applied during a forecast period. The output numerical value of differential production is added to the current production rate of the plants to obtain a new production rate which is applied during the forecast period.

FIELD OF THE INVENTION

The present invention relates to a method of controlling the productionof one or more liquid products produced by one or more plants to meet apre-specified liquid demand in which electrical power is consumed in theproduction of the liquid and the liquid is obtained for distributionfrom active production of the liquid and/or from a liquid storagecapacity. More particularly, the present invention relates to such amethod of control in which the control is provided by an expert systemcontroller that functions on the basis of fuzzy logic.

BACKGROUND OF THE INVENTION

There exist many automated production schemes for controlling theproduction of liquid products in which electrical power is consumed by aproduction facility in the production of the liquid products.

An example of such a production facility is a cryogenic air separationplant in which air is compressed and then cooled to a temperature levelthat is at or near dew point. The compressed and cooled air is thenrectified in one or more distillation columns that are commonly designedto fractionate the air into a nitrogen rich product, oxygen and nitrogenrich products or oxygen, nitrogen and argon rich products.

In any air separation plant, a large proportion of the electrical poweris consumed in powering the main air compressor used in compressing theair. As such, the electrical power constitutes the major cost ofproduction. In air separation plants that are designed to produce aliquid oxygen product, the liquid oxygen is a value added product inthat oxygen constitutes roughly 20 percent of the air to befractionated. In an air separation plant that is designed to produce aliquid nitrogen product, a nitrogen liquefier is employed that alsoconsumes electrical power.

Air separation plants are provided with a liquid storage capacity thatconsists of one or more liquid storage tanks that are capable of storingthe liquid products produced by the plant. Liquid products to bedistributed to meet customer demand may be produced from activeproduction of the facility and/or from stored liquid. Plant operatorstherefore must decide on how much liquid must be produced by the plantitself to meet such demand as opposed to the amount of liquid to bedrawn from storage.

Complicating the decision is that the electrical power costs are avariable factor that can change with the time of day. As indicatedabove, the cost of electrical power can be the major cost in theproduction of liquid by a plant and this is particularly true in airseparation plants. Additionally, many chemical plants that are designedto produce liquid products, air separation plants in particular, cannotbe controlled so that changes in active production are instantaneous. Incase of air separation plants, production rates cannot be rapidly swungwithout changing product purity. Hence, if liquid product is beingaccumulated or depleted from storage at a particular rate of change andbased upon a previous level of demand, any control input into the plantwill not be instantaneous and in any case adds a degree of freedom tothe problem that makes a decision on the level of plant liquidproduction particularly difficult.

Hence, a forecast by plant personnel on how much liquid product orproducts to be produced by a plant is a particularly difficult problemin which often plant production remains stable with production beingramped up and down to meet unusual, high and low demands. This leads toinefficient production on a monetary basis that often results in a plantnot capturing its potential profitability.

As will be discussed, the present invention provides a method ofcontrolling a plant or plants that produce one or more liquid productsby an expert system controller of control to meet projected customerdemand that simultaneously considers projected unit power costs, theamount of liquid within the plant liquid storage capacity and the rateof change within the plant liquid storage capacity. This allows theplant to be more efficiently operated on an economic basis.

SUMMARY OF THE INVENTION

The present invention provides a method of controlling the production ofat least one liquid product produced by at least one plant over aforecast time interval. The at least one plant has a liquid storagecapacity and consumes electrical power in the production of the at leastone liquid product.

In accordance with the method, continually, upon the elapse of acalculation time interval equal to the forecast time interval, a rate ofchange of liquid inventory of the at least one liquid product containedwith the liquid storage capacity is calculated over a past time intervalequal to the calculation time interval and ending upon the calculationof the rate of change of liquid inventory. Input linguistic valesreferable to current liquid inventory, the rate of change of liquidinventory, a current production rate of the at least one liquid productby the at least one plant, projected demand requirements for the atleast one liquid product and projected unit power costs for theelectrical power over the forecast time interval are all inputted intoan expert system controller employing fuzzy logic. The fuzzy logicapplies such input linguistic values to at least one rule set to obtainoutput linguistic values and to convert the output linguistic valuesinto an output numerical value of a forecasted differential productionrate for the at least one liquid product.

The at least one rule set and the input linguistic values are definedsuch that the forecasted differential production rate tends to decreaseas the rate of change and/or the liquid inventory and/or the projectedunit power costs increase and the differential production rate tends toincrease as the projected demand requirements increase and vice-versa.

The output numerical value of the forecasted differential productionrate is added to the current production rate to obtain the newproduction rate. The plant is controlled to produce the at least oneliquid product at the new production rate during the forecast timeinterval.

As can be appreciated from the above discussion, the level of controlexerted in the present invention allows more or less liquid to beproduced by the plant or plants. As the level of production falls, moreliquid will be supplied from the liquid storage capacity. Projecteddemand requirements are met both with plant production and prior plantproduction in the form of stored liquid. Hence, what is captured by thepresent invention is a level of control that intelligently modulatesproduction based not only on present demand but also past production inthe form of stored liquid or increasing levels of amounts of liquidbeing stored. In this manner alone, power costs can be reduced becausethere will tend to be lower production of liquid at higher amounts ofstored liquid and rates of change. Additionally, electrical power costsover at least the long term are also reduced in that assuming a lowlevel of projected unit power cost, more liquid will tend to be producedwith any excess going to liquid storage for potentially future use whenpower costs are higher.

In any embodiment of the present invention, the at least one plant canbe controlled by an open-loop response, namely, the numerical valuesproduced by expert system controller serve as a manual input by theoperator. Additionally, a closed-loop response is possible in whichcalculated new production rates are automatically fed as an input to aplant controller of the at least one plant.

Preferably, the at least one rule set is composed of three rule sets.The input numerical values for the current liquid inventory and the rateof change can be converted into a first set of input linguistic values.The input numerical values for the projected demand requirements and theprojected unit power costs can be converted into a second of the inputlinguistic values and a third of the input linguistic values,respectively. The first set of input linguistic values are applied tothe first of the three rule sets to obtain an intermediate linguisticvalue. The second of the input linguistic values and the firstintermediate linguistic value obtained from the first rule set can beapplied to the second of the three rule sets to obtain a secondintermediate linguistic value. Lastly, the third of the linguisticvalues and the second intermediate linguistic value can be applied tothe third of the three sets to obtain the output linguistic value.

In any embodiment of the present invention, a bounds check can beperformed on the new production rate to ascertain whether the newproduction rate is between upper and lower bounds of production of theat least one plant. The at least one plant can then be controlled toproduce that at least one liquid product at the new production rateduring the forecast period when said new production rate is between theupper and lower bounds of production. When the new production rate isbelow the lower of the bounds, the at least one plant is controlled toproduce that at least one liquid product at the lower of the bounds andwhen the new production rate is above the upper of the bounds, the atleast one plant is controlled to produce the at least one liquid productat the upper of the bounds.

The input of the input linguistic values into the expert systemcontroller can be accomplished, internally in the expert systemcontroller, by inputting input data of numerical values related to thecurrent liquid inventory, the rate of change, the projected demandrequirements and the unit power costs into the expert system controller.In such case, the expert system controller converts the input numericalvalues into the input linguistic values.

The foregoing method of control can be applied to a cryogenic airseparation plant. In such a plant, the forecasted demand requirementsfor the at least one liquid product can be based upon customer demandpatterns and any customer request for the at least one liquid productoccurring prior to the implementation of the new production rate. Inthis manner, it is not only actual demand that can be considered butalso projected demand on a historical basis. Preferably, the inputnumerical values can be converted into input linguistic values and theoutput linguistic values can be converted to the forecasted differentialproduction rate by input and output fuzzy sets that are of triangularconfiguration. The final linguistic value can be converted into theoutput numerical value by a center of area method.

BRIEF DESCRIPTION OF THE DRAWINGS

While the specification concludes with claims distinctly pointing outthe subject matter that Applicants regard as their invention, it isbelieved that the present invention will be better understood when takenin connection with the accompanying drawings in which:

FIG. 1 is a schematic illustration of an expert system controllerapplied to the control of an air separation plant that produces oneliquid product;

FIG. 2 is a logic flow diagram of the expert system controllerillustrated in FIG. 1;

FIG. 3 is a schematic illustration of the application of rule sets toinput data to obtain a new production rate for liquid over a forecastperiod;

FIG. 4 is a graphical representation of a fuzzy set utilized to convertcurrent liquid inventory to linguistic values;

FIG. 5 is a graphical representation of a fuzzy set that is used toconvert the rate of change of liquid inventory into linguistic values;

FIG. 6 is a graphical representation of a fuzzy set that is used toconvert the projected demand for liquid into linguistic values;

FIG. 7 is a graphical representation of a fuzzy set that is used toconvert the projected unit power costs into linguistic values; and

FIG. 8 is a graphical representation of a fuzzy set used to convertoutput linguistic values into an output numerical value of theforecasted differential production rate.

DETAILED DESCRIPTION

With reference to FIG. 1, a method of the present invention is appliedto control of an air separation plant 10. As indicated above, this isfor illustrative purposes. A method of the present invention can beapplied in any type of plant that produces at least one liquid productas indicated by liquid stream 12 by the consumption of electrical powerand that has a liquid storage capacity provided by liquid tank 14.

Air separation plant 10 has an air separation unit 16 that can be of anydesign. For example, air separation unit 16 could be a double columnarrangement in which higher and lower pressure columns, that is columnsthat operate at higher and lower pressures, are connected to one anotherin a heat transfer relationship by a condenser-reboiler. Air isfiltered, compressed by a main air compressor, that consumes most of theelectrical power requirements, and is cooled to at or near its dewpoint. The compressed air is introduced into the base of a higherpressure column to initiate the formation of an ascending vapor phasethat becomes evermore rich in nitrogen and lean in oxygen. The nitrogenoverhead in the higher pressure column is condensed in the condenserreboiler to initiate the formation of a descending liquid phase thatbecomes evermore rich in oxygen as it descends. The nitrogen overhead isthen further refined in the lower pressure column to produce liquidoxygen that may be taken as a product and that condenses the toweroverhead in the higher pressure column. Liquid-vapor contact within suchcolumns is provided by trays or packing.

In accordance with the present invention an expert system controller 18is used to determine the production rate of liquid of air separationplant 10 for a forecast time interval. The liquid can be liquid oxygen,as described above. Additionally though, liquid nitrogen could beproduced by a nitrogen liquefier. Expert system controller 18 is knownin the art as an expert system controller that functions on the basis offuzzy logic. Expert system controller 18 is in practice a computerprogram that is loaded into a personal computer that preferably over alocal area network ties in with a distributed control system for plant10. An example of this program is the Fuzzy Logic Toolbox for MATLABthat can be obtained from The MathWorks, Inc. (3 Apple Hill Drive,Natick, Mass. 01760-2098).

Numerical values of a projected unit power cost 20 and projected demandrequirements 22 serve as an input to expert system controller 18.Additionally, the current rate of production of the liquid productproduced by plant 10 is also an input 24 into expert system controller18. In this regard, the current production is a flow rate of liquidstream 10 that is converted into an electrical signal referable to theflow rate by a flow meter 26. Additional inputs are the liquid inventoryof the liquid product as an input 28 and the rate of change of liquidinventory liquid as an input 30. The inventory calculation in block 32is based upon a level sensed by level transducer 34 which is preferablya set of differential pressure transducers as known in the art. The rateof change of liquid inventory is computed in box 36 labeled “d/dt” inwhich a previous liquid inventory, that was determined of a timeprevious to execution of controller 18 in an amount equal to thecalculation of time interval, is subtracted from the current liquidinventory (input 28) and divided by the time of the calculationinterval.

Input 28 and input 30 can be data that is automatically read uponexecution of expert system controller 18. Preferably, however, datareferable to all of inputs can be obtained from a process historianconnected to the distributed control system or a process historiancontained in expert system controller 18 to store past values of liquidinventory in its own memory space.

The projected unit power cost input 20 is an expected unit power costwhich can be obtained from an electrical utility, other data orforecast. The projected demand requirements 20 can be based on customerdemand patterns for the liquid as well as instantaneous orders fromcustomers that must be fulfilled within the forecast time interval.Additionally, telemetry of liquid storage tanks at customer sites canalso be used to determine such demands. Typically, projected liquiddemand can be computed from customer demand patterns and any requestsfor liquid.

Although as indicated above, the data input to expert system controller18 may be automatic, such data input could be manual as well. Theforecast period and therefore the calculation period can preferably beanywhere from 1 to 24 hours and is most preferably the length of a shiftwhich can be approximately 12 hours.

With reference to FIG. 2, data is read or alternatively inputted at 38into expert system controller 18 and an average rate of change of liquidinventory is calculated at 40. This average is simply the differencebetween a past liquid inventory within liquid storage tank 14 and thecurrent liquid inventory within liquid tank 10 divided by thecalculation time interval, or in other words, the time period betweenexecution of the programming involved in expert system controller 18. Asindicated above, this time period could be a shift length which would be12 hours. As will be discussed and as indicated at 42, the numericalvalues for the rate of change of liquid inventory input 30 and thecurrent liquid inventory input 28 are fuzzified, or in other words,converted into linguistic values by graphical representations of fuzzysets.

With additional reference to FIG. 3, the fuzzification of the currentliquid inventory input 28 and rate of change of liquid inventory input30 produce first and second input linguistic values 46 and 48 that areapplied to an applicable rule set 50 such as will be discussed withreference to Table 1 below to produce first intermediate linguisticvalues 52. Numerical values for projected liquid demand input 22 arethen converted into third linguistic values 54 which are appliedtogether with first intermediate linguistic values 52 to a second ruleset 56 that will be discussed with reference to Table 2 below to producesecond intermediate linguistic values 58. Numerical values for projectedpower cost 20 are then converted into fourth linguistic values 60 whichtogether with second intermediate linguistic values 58 are applied to arule set such as set forth in Table 3 below to obtain output linguisticvalues 64.

With reference again to FIG. 2, the output linguistic values 64 are thenconverted into an output numerical value for the differential productionrate of the liquid product 12 as indicated in block 66. As indicated at68, the numerical value for the forecasted differential production rate64 of the liquid product 12 is then added to the current liquidproduction rate provided as input 24. This sum is the new productionrate to be applied to plant 10 during the forecast period. At stage 69of the execution of the expert system controller 18, the program remainsdormant for the calculation time interval and then executes again asdescribed above to determine a new production rate for a successiveforecast period.

At 68 bounds checking is performed. While in most cases, the newproduction rate will be within the upper and lower limits of the liquidproduction from the air separation plant 10, it is preferred that asimple bounds check be performed to confirm that the new production rateis within such bounds of production before it is set to the controlsystem. If the new production rate is greater than the maximum or lessthan the minimum, the respective bound is outputted. If the calculatedvalues are between the minimum and maximum, the new production rate isoutputted unchanged to a controller 70. Once the value is outputted tothe plant information system, it can be used in one or two ways. In anopen-loop situation the plant can use the output as a guideline to setthe liquid production of the plant. This approach requires manual humanintervention. The second way, closed-loop, the controller output goesdirectly to the supervisory system. The controller 70 can be either amodel predictive controller “MPC” or a real time optimization program“RTO” that automatically changes the plant operations.

Although as has been discussed above, expert system controller 18fuzzifies numerical values for projected power cost, projected liquidproduct demand and etc., the operator might manually convert thenumerical values to linguistic values and manually input such valuesinto the expert system controller 18.

By way of example and with specific reference to FIG. 4, assuming a tanksize for liquid tank 14 of 60 MMSCF, fuzzy sets for the current liquidinventory (input 28) could be defined with a low limit equal to 30, atarget equal to 40 and a high limit equal to 50. With respect to FIG. 5,the rate of change of the liquid inventory (input 30) would then bedefined using numerical values “CN”=−2 and “CP”=+2 with the rate ofchange calculated using 12 hours. Each input value is described by itsmembership in each of these sets. For instance, if liquid inventory iscurrently 45 MSSCF, then with reference to FIG. 4, the resultant firstlinguistic values could be defined as: as 0 percent “low”, 50 percent“good” and 50 percent “high”. In standard notation this would beexpressed as (0.5, “good”) and (0.5, “high”). The same procedure wouldbe calculated for the rate of change of liquid inventory. Assuming thatthe calculated rate of change of the inventory 30 is 1.5, then thesecond linguistic values could be expressed as follows: (0.5, “zero”)and (0.5, “positive”).

All sets are then applied, as indicated in block 44, to determine aforecasted differential production rate. Although one rule set could beused, practically three rule sets are used. With reference to FIG. 3,the first linguistic values 46, related to current liquid inventory ofliquid tank 14 and second linguistic values 48, related to the rate ofchange of liquid inventory within liquid tank 14, are applied to a ruleset shown below in Table 1 to obtain first intermediate linguisticvalues M₁, designated by reference number 52.

TABLE 1 If Inventory If ROC of Rule is Inventory is Then M₁ should be: 1“High” “Positive” “Very Negative” 2 “High” “Zero” “Negative” 3 “High”“Negative” “Zero” 4 “Good” “Positive” “Negative” 5 “Good” “Zero” “Zero”6 “Good” “Negative” “Positive” 7 “Low” “Positive” “Zero” 8 “Low” “Zero”“Positive” 9 “Low” “Negative” “Very Positive”In the example, rules 1, 2, 4 and 5 are applicable. Rule 1 states, ifthe inventory is “high” and rate of change in the inventory (“ROC”) is“positive”, the intermediate value M₁ is “very negative”. Theapplicability of the rule needs to be quantified. This is accomplishedby taking the rate of change of the inventory is (0.5, “zero”) and (0.5,“positive”). The intersection of the two fuzzy sets “inventory is high”and “change in inventory is positive” is the minimum of their respectivememberships. In this case the value is 0.5. In a similar way, rules 2,4, and 5 are all calculated to have an applicability of 0.5. Thesevalues are then normalized to 1 for the sake of simplicity.

Once the applicable rules are determined and their degree quantified,the output from this rule set, the first intermediate linguistic value52 (M₁) can be characterized. Each of the rules listed give a linguisticvalue for M₁. For instance, rule 1 states that M₁ should be “verynegative”, rules 2 and 4 state “negative” and rule 5 states “zero”. Thecharacterization of M₁ is simply the sum of the applicability of therules which dictate a certain fuzzy set and then normalized so that thesum of applicability is equal to 1. In this case, M₁ would be (0.25,“very negative”), (0.5, “negative”), and (0.25, “zero”).

If the fuzzy sets graphically illustrated in FIGS. 4 and 5 and the ruleset of Table 1 are closely inspected, what is apparent is that as thecurrent liquid inventory (input 28) goes to the high limit and the rateof change of the liquid inventory (input 30) becomes the most positive,then the first and second linguistic values tend towards high andpositive. What this means is that the linguistic values are expressingthat the liquid level is tending towards high in liquid tank 14 and theliquid level 28 within liquid tank 14 is rapidly rising. When “high” and“positive” are applied to the rule set of Table 1, it can be seen thatM₁ tends toward “negative” and “very negative”. With reference to FIG.8, these linguistic values have the effect of decreasing the forecasteddifferential liquid production rate 64 by making them successively morenegative. Hence, in this example, or in any embodiment of the inventionfor that matter, it can be said that the fuzzy sets and rule setsapplicable to the current liquid inventory and the rate of change ofliquid inventory are selected so that increased current liquid inventoryand more positive rates of change of liquid inventory tend to yield adecrease in the forecasted differential production rate.

As well known in the art of programming an expert system, the bounds andslope of lines used in the fuzzy sets 4 and 5 and the rule set of Table1 are selected on the basis of past plant operation and common sense.Hence the fuzzy sets and rule sets will not necessarily be the same fordifferent plants and applications of the present invention. The sameholds true with respect to other fuzzy sets and rule sets discussedbelow.

A further point here with respect to FIG. 4 is the setting of the “lowlimit” and the “high limit”. The “low limit” in case of a liquid onlyplant might be the liquid level necessary to meet ordinary demands for agiven time period, for example, a shift of 12 hours. In a plant thatalso makes gaseous products, there is a certain amount of liquid thatmust always be kept on hand that is capable of being vaporized to meetgas supply contracts. In such case, the “low limit” might be set at suchlevel or a combination of a level of liquid necessary to meet gas supplycontracts and to supply liquid to meet demand patterns. The “high level”is simply a constraint on the maximum liquid inventory able to be storedin liquid tank 14.

With reference to FIG. 6, numerical values for the projected liquiddemand (input 22) are converted into third linguistic values 54 andtogether with first intermediate linguistic values 52 are applied to asecond rule set 56 given in Table 2 below. In FIG. 6, for exemplarypurposes, S_(L)=3 mscf; S_(A)=5 mscf; and S_(H)=7 mscf. Assuming theprojected liquid demand 22 is 5 mscf or average, then the correspondinglinguistic value obtained from FIG. 6 would be (1.0, “average”). Asindicated above, the result is applied to Table 2 below:

TABLE 2 M₁ S VP P Z N VN H VP VP P Z N A VP P Z N VN L P Z N VN VNIn Table 2: S is projected liquid demand; VP is “very positive”; P is“positive”; Z is “zero”; N is “negative”; VN is: “very negative”; H is:“high”; A is “Average”; and L is “low”.

Applying the rule set of Table 2, second intermediate linguistic values58 are obtain as follows: (0.25 “very negative”); (0.5 “negative”); and(0.25 positive). It is to be noted that since the projected liquiddemand 22 is average, the first intermediate linguistic values 52 aresimply passed through as a result of the application of this rule set. Afurther point to be noted is that as the levels of projected liquiddemand 22 tend towards high, the results tend towards “positive” whichincrease the forecasted differential production rate 64.

With reference to FIG. 7, the linguistic set is shown to convertprojected unit power costs (input 20) to fourth linguistic values 60. InFIG. 7, again for exemplary purposes, P_(L)=$20.00/MW; P_(A)=$35.00/MW;and P_(H)=$50.00/MW. The resultant linguistic values of projected unitpower cost obtained from FIG. 7 and the second intermediate linguisticvalues 58 are applied to a third rule set given in Table 3 below:

TABLE 3 M₂ C VP P Z N VN L VP VP P Z N A VP P Z N VN H P Z N VN VNThe notation used in Table 3 is the same as that used in Table 2. “C”are the fourth linguistic values 60 of the projected unit power costs.Assuming power costs are at $50.00/MW, then the fourth linguistic valuesare (1.0, “High”). Applying this with second intermediate linguisticvalues 58 to the third rule set, the output linguistic values 64 are(0.75, “very negative); (0.25, “negative”). It is to be noted that asthe projected unit power costs tends towards high, the output linguisticvalues tend towards “very negative”, that has the effect of decreasingthe differential production rate.

With reference to FIG. 8, the output linguistic values are convertedback to a numerical value to obtain the forecasted differentialproduction rate 64. In FIG. 8, for exemplary purposes, dF_(MIN)=−30,dF_(VN)=−20, dF_(N)=−10, dF_(P)=10, dF_(VP)=20, dF_(MAX)=30. The outputis converted to a numerical value using the common and simplecenter-of-gravity approach. In that approach the center of gravity ofeach of the membership sets is determined. The numerical value of theoutput is then the sum of the centers of gravity multiplied by themembership in that set. In this example, the centers of gravity are −10for “negative”, and −22.5 for “very negative”. The weighted sum of thecenter of gravities is calculated as: (0.25)(−10)+(0.75)(−22.5)=−19.375.Therefore the change in the liquid production rate should be −19.375mcfh/hour and the same when added to the current liquid production rate24 will reduce the production rate of plant 10 over the forecast timeinterval and as such, demand requirements will be met more from liquidtank 14 than from plant 10.

Many different types of fuzzy sets can be used to describe controllerinputs and outputs. Additional types of fuzzy sets include trapezoidaland gaussian. Also there are different methods for defuzzifying theoutput of the controller. Other methods include center-of-large areamethod and middle-of-maximum method.

The controller can be applied to multiple plants which feed a commonliquid inventory. In that case, the controller would be working with thetotal liquid production of all the plants involved. The controllercalculations would not be changed in any way. The output of thecontroller would need to be split amongst the various production plants,either manually or through some supervisory control system that operatedeither as an MPC or an RTO.

While the present invention has been described with reference to apreferred embodiment, as will occur to those skilled in the art,numerous changes, additions and omissions may be made without departingfrom the spirit and scope of the invention.

1. A method of controlling the production of at least one liquid productproduced by at least one plant over a forecast time interval, the atleast one plant having a liquid storage capacity and consumingelectrical power to produce the at least one liquid product, said methodcomprising: continually, upon the elapse of a calculation time intervalequal to the forecast time interval: calculating a rate of change ofliquid inventory of the at least one liquid product contained with theliquid storage capacity over a past time interval equal to thecalculation time interval and ending upon the calculation of the rate ofchange of liquid inventory; inputting input linguistic values referableto current liquid inventory, the rate of change of liquid inventory, acurrent production rate of the at least one liquid product by the atleast one plant, projected demand requirements for the at least oneliquid product and projected unit power costs for the electrical powerover the forecast time interval into an expert system controlleremploying fuzzy logic to apply the input linguistic values to at leastone rule set to obtain output linguistic values and to convert theoutput linguistic values into an output numerical value of a forecasteddifferential production rate for the at least one liquid product; the atleast one rule set and the input linguistic values defined such that theforecasted differential production rate tends to decrease as the rate ofchange and/or the liquid inventory and/or the projected unit power costsincrease and the differential production rate tends to increase as theprojected demand requirements increase and vice-versa; adding the outputnumerical value of the forecasted differential production rate to thecurrent production rate to obtain a new production rate; and controllingthe plant to produce the at least one liquid product at the newproduction rate during the forecast time interval.
 2. The method ofclaim 1, wherein the at least one plant is controlled by an open-loopresponse.
 3. The method of claim 1, wherein the at least one plant iscontrolled by a closed-loop response.
 4. The method of claim 1, furthercomprising: performing a bounds check on the new production rate toascertain whether said new production rate is between the upper andlower bounds of production of the at least one plant; controlling the atleast one plant to produce the at least one liquid product at the newproduction rate during the forecast period when said new production rateis within the upper and lower bounds of production; controlling the atleast one plant to produce the at least one liquid product at the lowerof the bounds of production when the new production rate is below thelower of the bounds; and controlling the at least one plant to producethe at least one liquid product at the upper of the bounds of productionwhen the new production rate is above the upper of the bounds.
 5. Themethod of claim 4, wherein the input of the input linguistic values intothe expert system controller is accomplished, internally in the expertsystem controller, by inputting input data of numerical values relatedto the current liquid inventory, the rate of change, the projecteddemand requirements and the unit power costs into the expert systemcontroller and the expert system controller converting the inputnumerical values into the input linguistic values.
 6. The method ofclaim 4, wherein the at least one plant is a cryogenic air separationplant.
 7. The method of claim 6, wherein the input of the inputlinguistic values into the expert system controller is accomplished,internally in the expert system controller, by inputting input data ofnumerical values related to the current liquid inventory, the rate ofchange, the projected demand requirements and the unit power costs intothe expert system controller and the expert system controller convertingthe input numerical values into the input linguistic values.
 8. Themethod of claim 7, wherein the forecasted demand requirements for the atleast one liquid product are based upon customer demand patterns and anycustomer requests for the at least one liquid product occurring prior tothe implementation of the new production rate.
 9. The method of claim 8,wherein the input numerical values are converted into linguistic valuesand the output linguistic value is converted to the forecasteddifferential production rate by input and output fuzzy sets oftriangular configuration and the final linguistic value is converted tothe output numerical value by a center of area method.
 10. The method ofclaim 9, wherein the at least one plant is controlled by an open-loopresponse.
 11. The method of claim 10, wherein the at least one plant iscontrolled by a closed-loop response.